1 Prerequisites

1.1 Materials

Please visit https://github.com/arifpras/KelasData for the datasets and other relevant materials to be used during the sessions.

1.2 Clearing the environment

rm(list=ls())
ls()

## character(0)

1.3 Packages and libraries

#setting working directory
setwd("/Users/arifpras/OneDrive - The University of Nottingham/BB_KelasData/KelasData/00_Datasets")

1.4 Datasets

op_sales <- readxl::read_excel(path = "/Users/arifpras/OneDrive - The University of Nottingham/BB_KelasData/KelasData/00_Datasets/OP_all.xlsx", sheet = "OP_sales")

glimpse(op_sales)

## Rows: 51
## Columns: 8
## $ volume          <dbl> 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63…
## $ title           <chr> "Arriving Once Again", "The 11 Supernovas", "Roger and…
## $ release_date    <dttm> 2008-06-04, 2008-09-04, 2008-12-04, 2009-03-04, 2009-…
## $ pages           <dbl> 216, 232, 216, 216, 216, 200, 216, 216, 216, 216, 216,…
## $ chapters        <dbl> 10, 11, 10, 10, 10, 9, 10, 11, 11, 11, 10, 9, 11, 12, …
## $ sales_mio       <dbl> 1.441785, 1.517953, 1.476194, 1.641952, 1.609125, 1.66…
## $ last_movie_days <dbl> 95, 187, 278, 368, 460, 552, 643, 82, 174, 235, 327, 4…
## $ vixi_avg        <dbl> 23.60, 39.95, 48.06, 42.48, 28.58, 24.40, 20.15, 17.16…

#change the date format: from <POSITX> to <date>
op_sales <- op_sales %>% 
  mutate(release_date = as.Date(release_date))

glimpse(op_sales)

## Rows: 51
## Columns: 8
## $ volume          <dbl> 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63…
## $ title           <chr> "Arriving Once Again", "The 11 Supernovas", "Roger and…
## $ release_date    <date> 2008-06-04, 2008-09-04, 2008-12-04, 2009-03-04, 2009-…
## $ pages           <dbl> 216, 232, 216, 216, 216, 200, 216, 216, 216, 216, 216,…
## $ chapters        <dbl> 10, 11, 10, 10, 10, 9, 10, 11, 11, 11, 10, 9, 11, 12, …
## $ sales_mio       <dbl> 1.441785, 1.517953, 1.476194, 1.641952, 1.609125, 1.66…
## $ last_movie_days <dbl> 95, 187, 278, 368, 460, 552, 643, 82, 174, 235, 327, 4…
## $ vixi_avg        <dbl> 23.60, 39.95, 48.06, 42.48, 28.58, 24.40, 20.15, 17.16…

(
  db01 <- op_sales %>%
    pivot_longer(
      cols = pages:vixi_avg,
      names_to = "obsvar",
      values_to = "obsval"
    ) %>%
    mutate(
      obsvar = recode(
        obsvar,
        chapters = "Chapters",
        last_movie_days = "Days from Last Movie",
        pages = "Pages",
        sales_mio = "Total Sales (in millions)",
        vixi_avg = "VIX Index (in average)"
      )
    )
)

2 Plotting

2.1 Line chart

(
  plot01 <- db01 %>%
    #plot the dataset
    ggplot(aes(x = volume, y = obsval)) +
    geom_line(aes(color = obsvar)) +
    facet_wrap( ~ obsvar, scales = "free", nrow = 2) +
    theme_light() +
    labs(x = "\nVolume", y = "") +
    #ggthemes::scale_color_colorblind() +
    scale_color_brewer(palette = "Set1") +
    theme(
      axis.text.x = element_text(size = 8),
      axis.ticks.x = element_blank(),
      axis.line.x = element_blank(),
      axis.title.x = element_text(size = 7),
      axis.line.y = element_blank(),
      axis.title.y = element_text(size = 7),
      axis.text.y = element_text(size = 8),
      plot.title.position = "plot",
      plot.caption.position = "plot",
      legend.title = element_text(size = 7),
      legend.text = element_text(size = 7),
      legend.key.size = unit(0.5, "cm"),
      legend.spacing = unit(1, "cm"),
      plot.title = element_text(hjust = 0, size = 13, face = "bold"),
      plot.subtitle = element_text(hjust = 0, size = 12),
      plot.caption = element_text(hjust = 0, size = 12),
      # legend.title = element_blank(),
      legend.position = "bottom",
      strip.text.x = element_text(size = 8),
      panel.grid.major.y = element_line(colour = "grey97"),
      panel.ontop = FALSE,
      panel.spacing = unit(1, "lines")
    ) +
    guides(color = guide_legend(title = "Observable variables:"))
)

2.2 Box plot

pacman::p_load(colorspace)
(
  plot02 <- db01 %>%
    #plot the dataset
    ggplot(aes(x = volume, y = obsval)) +
    geom_boxplot(aes(
      color = obsvar,
      fill = after_scale(desaturate(lighten(color, .7), .7))
      ),
      size = 1) +
    #geom_line(aes(color = obsvar)) +
    facet_wrap(~ obsvar, scales = "free", nrow = 2) +
    theme_light() +
    labs(
      title = "Boxplot",
      x = "\nVolume", y = "") +
    #ggthemes::scale_color_colorblind() +
    scale_color_brewer(palette = "Set1") +
    theme(
      axis.text.x = element_text(size = 8),
      axis.ticks.x = element_blank(),
      axis.line.x = element_blank(),
      axis.title.x = element_text(size = 7),
      axis.line.y = element_blank(),
      axis.title.y = element_text(size = 7),
      axis.text.y = element_text(size = 8),
      plot.title.position = "plot",
      plot.caption.position = "plot",
      legend.title = element_text(size = 7),
      legend.text = element_text(size = 7),
      legend.key.size = unit(0.5, "cm"),
      legend.spacing = unit(1, "cm"),
      plot.title = element_text(hjust = 0, size = 16, face = "bold"),
      plot.subtitle = element_text(hjust = 0, size = 12),
      plot.caption = element_text(hjust = 0, size = 12),
      # legend.title = element_blank(),
      legend.position = "bottom",
      strip.text.x = element_text(size = 8),
      panel.grid.major.y = element_line(colour = "grey97"),
      panel.ontop = FALSE,
      panel.spacing = unit(1, "lines")
      ) +
    guides(color = guide_legend(title = "Observable variables:"))
)

2.3 Histogram

#pacman::p_load(colorspace)
(
  plot03 <- db01 %>%
    #plot the dataset
    ggplot(aes(x = obsval)) +
    geom_histogram(bins = 50, 
                   aes(
                     color = obsvar,
                     fill = after_scale(desaturate(lighten(color, .7), .7))
                     )) +
    facet_wrap(~ obsvar, scales = "free", nrow = 2) +
    theme_light() +
    labs(
      title = "Histogram",
      x = "\nRelease Date", y = "") +
    #ggthemes::scale_color_colorblind() +
    scale_color_brewer(palette = "Set1") +
    theme(
      axis.text.x = element_text(size = 8),
      axis.ticks.x = element_blank(),
      axis.line.x = element_blank(),
      axis.title.x = element_text(size = 7),
      axis.line.y = element_blank(),
      axis.title.y = element_text(size = 7),
      axis.text.y = element_text(size = 8),
      plot.title.position = "plot",
      plot.caption.position = "plot",
      legend.title = element_text(size = 7),
      legend.text = element_text(size = 7),
      legend.key.size = unit(0.5, "cm"),
      legend.spacing = unit(1, "cm"),
      plot.title = element_text(hjust = 0, size = 16, face = "bold"),
      plot.subtitle = element_text(hjust = 0, size = 12),
      plot.caption = element_text(hjust = 0, size = 12),
      # legend.title = element_blank(),
      legend.position = "bottom",
      strip.text.x = element_text(size = 8),
      panel.grid.major.y = element_line(colour = "grey97"),
      panel.ontop = FALSE,
      panel.spacing = unit(1, "lines")
      ) +
    guides(color = guide_legend(title = "Observable variables:"))
)

3 Descriptive statistics

3.1 Summary: skim

op_sales %>% select(release_date, pages, chapters, sales_mio, last_movie_days, vixi_avg) %>% 
  skimr::skim()

Data summary
Name	Piped data
Number of rows	51
Number of columns	6
_______________________
Column type frequency:
Date	1
numeric	5
________________________
Group variables	None

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
release_date	0	1	2008-06-04	2021-09-03	2014-09-04	51

Variable type: numeric

skim_variable	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
pages	1	214.90	11.09	192.00	208.00	216.00	216.00	248.00	▃▂▇▂▁
chapters	1	10.47	0.67	9.00	10.00	10.00	11.00	12.00	▁▇▁▆▁
sales_mio	1	2.22	0.45	1.44	1.78	2.33	2.61	2.90	▇▅▃▇▇
last_movie_days	1	485.86	326.63	44.00	230.00	419.00	673.00	1297.00	▇▆▅▂▂
vixi_avg	1	19.90	8.57	10.55	14.14	16.77	23.10	48.06	▇▃▁▁▁

3.2 Summary: stargazer

pacman::p_load(pastecs, stargazer)

(
  summ01 <- op_sales %>%
    select(pages:vixi_avg) %>%
    rename("Chapters" = chapters,
           "Days from Last Movie" = last_movie_days,
           "Pages" = pages,
           "Total Sales (in millions)" = sales_mio,
           "VIX Index (in average)" = vixi_avg)
)

summ01 %>%
  as.data.frame() %>% 
  stargazer(type = 'text', out = "descsumm.txt", digits = 1) #flip = TRUE

## 
## ========================================================================
## Statistic                 N  Mean  St. Dev. Min  Pctl(25) Pctl(75)  Max 
## ------------------------------------------------------------------------
## Pages                     51 214.9   11.1   192    208      216     248 
## Chapters                  51 10.5    0.7     9      10       11     12  
## Total Sales (in millions) 51  2.2    0.4    1.4    1.8      2.6     2.9 
## Days from Last Movie      51 485.9  326.6    44    230      673    1,297
## VIX Index (in average)    51 19.9    8.6    10.5   14.1     23.1   48.1 
## ------------------------------------------------------------------------

3.3 Summary: dplyr

(
  summ02 <- db01 %>%
    group_by(obsvar) %>%
    summarise(
      Obs = n(),
      Mean = mean(obsval, na.rm = TRUE),
      Std.Dev = sd(obsval, na.rm = TRUE),
      Min. = min(obsval, na.rm = TRUE),
      Max. = max(obsval, na.rm = TRUE),
      P25 = quantile(obsval, 0.25, na.rm = TRUE),
      P75 = quantile(obsval, 0.75, na.rm = TRUE),
      Var. = var(obsval, na.rm = TRUE)
  )
)

3.4 Summary: parameters

pacman::p_load(parameters)

(
  summ03 <- op_sales %>%
    select(pages:vixi_avg) %>%
    rename("Chapters" = chapters,
           "Days from Last Movie" = last_movie_days,
           "Pages" = pages,
           "Total Sales (in millions)" = sales_mio,
           "VIX Index (in average)" = vixi_avg) %>%
    describe_distribution() %>% 
    t()
)

##           [,1]          [,2]          [,3]                       
## Variable  "Pages"       "Chapters"    "Total Sales (in millions)"
## Mean      "214.901961"  " 10.470588"  "  2.220214"               
## SD        " 11.0873891" "  0.6738825" "  0.4455385"              
## IQR       "  8.000000"  "  1.000000"  "  0.850304"               
## Min       "192.000000"  "  9.000000"  "  1.441785"               
## Max       " 248.000000" "  12.000000" "   2.901059"              
## Skewness  " 0.3962788"  " 0.3149269"  "-0.2057600"               
## Kurtosis  " 0.5150045"  "-0.0597263"  "-1.3611366"               
## n         "51"          "51"          "51"                       
## n_Missing "0"           "0"           "0"                        
##           [,4]                   [,5]                    
## Variable  "Days from Last Movie" "VIX Index (in average)"
## Mean      "485.862745"           " 19.899804"            
## SD        "326.6287813"          "  8.5725010"           
## IQR       "451.000000"           "  9.290000"            
## Min       " 44.000000"           " 10.550000"            
## Max       "1297.000000"          "  48.060000"           
## Skewness  " 0.6986392"           " 1.5833807"            
## Kurtosis  "-0.3056124"           " 2.1995930"            
## n         "51"                   "51"                    
## n_Missing "0"                    "0"

4 Correlations

library(corrr)

## 
## Attaching package: 'corrr'

## The following object is masked from 'package:skimr':
## 
##     focus

(
  corr01 <- op_sales %>% 
    select(pages:vixi_avg) %>%
    rename("Chapters" = chapters,
           "Days from Last Movie" = last_movie_days,
           "Pages" = pages,
           "Total Sales (in millions)" = sales_mio,
           "VIX Index (in average)" = vixi_avg) %>% 
    correlate() %>% 
    shave() %>% 
    fashion()
)

## 
## Correlation method: 'pearson'
## Missing treated using: 'pairwise.complete.obs'

5 Simple Regressions

5.1 Modelling: base

ols01bs <- lm(sales_mio ~ chapters + last_movie_days + vixi_avg, 
                data = op_sales)

summary(ols01bs)

## 
## Call:
## lm(formula = sales_mio ~ chapters + last_movie_days + vixi_avg, 
##     data = op_sales)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.74522 -0.31147 -0.04718  0.34711  0.76190 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      1.6107927  0.9671887   1.665  0.10248   
## chapters         0.1028203  0.0879605   1.169  0.24832   
## last_movie_days -0.0001212  0.0001866  -0.649  0.51928   
## vixi_avg        -0.0205174  0.0071327  -2.877  0.00603 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.4149 on 47 degrees of freedom
## Multiple R-squared:  0.1847, Adjusted R-squared:  0.1326 
## F-statistic: 3.548 on 3 and 47 DF,  p-value: 0.02135

ols02bs <- lm(sales_mio ~ pages + last_movie_days + vixi_avg, 
                data = op_sales)

summary(ols02bs)

## 
## Call:
## lm(formula = sales_mio ~ pages + last_movie_days + vixi_avg, 
##     data = op_sales)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.73780 -0.24534 -0.03507  0.26933  0.70463 
## 
## Coefficients:
##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)     -4.963e-01  1.099e+00  -0.451  0.65377   
## pages            1.481e-02  4.997e-03   2.964  0.00475 **
## last_movie_days -6.218e-05  1.750e-04  -0.355  0.72402   
## vixi_avg        -2.194e-02  6.591e-03  -3.329  0.00170 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3864 on 47 degrees of freedom
## Multiple R-squared:  0.2931, Adjusted R-squared:  0.248 
## F-statistic: 6.497 on 3 and 47 DF,  p-value: 0.0009077

5.2 Modelling: dplyr

pacman::p_load(broom)
(
  ols01dp <- op_sales %>%
    select(pages:vixi_avg) %>%
    lm(sales_mio ~ chapters + last_movie_days + vixi_avg, 
       data = .) %>%
    tidy()
)

(
  ols02dp <- op_sales %>%
    select(pages:vixi_avg) %>%
    lm(sales_mio ~ pages + last_movie_days + vixi_avg, 
       data = .) %>%
    tidy()
)

5.3 Modelling: estimatr

pacman::p_load(estimatr)

(
  ols01es <- op_sales %>%
    lm_robust(sales_mio ~ chapters + last_movie_days + vixi_avg,
              data = . ,se_type = "stata") %>%
    tidy()
)

pacman::p_load(estimatr)

(
  ols02es <- op_sales %>%
    lm_robust(sales_mio ~ pages + last_movie_days + vixi_avg,
              data = . ,se_type = "stata") %>%
    tidy()
)

5.4 Export: stargazer

stargazer(ols01bs, ols02bs, type = "text", out = "ols_base.txt")

## 
## ==========================================================
##                                   Dependent variable:     
##                               ----------------------------
##                                        sales_mio          
##                                    (1)            (2)     
## ----------------------------------------------------------
## chapters                          0.103                   
##                                  (0.088)                  
##                                                           
## pages                                          0.015***   
##                                                 (0.005)   
##                                                           
## last_movie_days                  -0.0001        -0.0001   
##                                  (0.0002)      (0.0002)   
##                                                           
## vixi_avg                        -0.021***      -0.022***  
##                                  (0.007)        (0.007)   
##                                                           
## Constant                          1.611         -0.496    
##                                  (0.967)        (1.099)   
##                                                           
## ----------------------------------------------------------
## Observations                        51            51      
## R2                                0.185          0.293    
## Adjusted R2                       0.133          0.248    
## Residual Std. Error (df = 47)     0.415          0.386    
## F Statistic (df = 3; 47)         3.548**       6.497***   
## ==========================================================
## Note:                          *p<0.1; **p<0.05; ***p<0.01

5.5 Export: texreg

pacman::p_load(texreg)

screenreg(list(ols01bs, ols02bs), digits = 2)

## 
## ===================================
##                  Model 1   Model 2 
## -----------------------------------
## (Intercept)       1.61     -0.50   
##                  (0.97)    (1.10)  
## chapters          0.10             
##                  (0.09)            
## last_movie_days  -0.00     -0.00   
##                  (0.00)    (0.00)  
## vixi_avg         -0.02 **  -0.02 **
##                  (0.01)    (0.01)  
## pages                       0.01 **
##                            (0.00)  
## -----------------------------------
## R^2               0.18      0.29   
## Adj. R^2          0.13      0.25   
## Num. obs.        51        51      
## ===================================
## *** p < 0.001; ** p < 0.01; * p < 0.05

#knitreg(list(ols01bs, ols02bs), digits = 2)
#wordreg(list(ols01bs, ols02bs), digits = 2, file = "ols_reg.doc")
plotreg(list(ols01bs, ols02bs), digits = 2)

## Models: bars denote 0.5 (inner) resp. 0.95 (outer) confidence intervals (computed from standard errors).

5.6 Export: `lm_robust` & `wordreg`

#doesn't work: lm_robust & stargazer & texreg
ols03es <- lm_robust(sales_mio ~ chapters + last_movie_days + vixi_avg,
              data = op_sales, se_type = "stata")

ols04es <- lm_robust(sales_mio ~ pages + last_movie_days + vixi_avg,
              data = op_sales, se_type = "stata")

#stargazer(ols03es, ols04es, type = "text", out = "ols_estimatr.txt")
#wordreg(list(ols03es, ols04es), digits = 2, file = "ols_estimatr.doc")

5.7 Plot: sjPlot single

#source: http://www.strengejacke.de/sjPlot/index.html
pacman::p_load(sjPlot, sjmisc, sjlabelled)
theme_set(theme_test())

plot_model(ols01bs,
           title = "Sales (in mio)",
           show.values = TRUE, 
           type = "est", 
           digits = 4) +
  #edit with ggplot2
  scale_y_continuous(limits = c(-0.12,0.12))

## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

plot_model(ols02bs,
           title = "Sales (in mio)",
           show.values = TRUE, 
           type = "est", 
           digits = 4) +
  #edit with ggplot2
  scale_y_continuous(limits = c(-0.12,0.12))

## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

5.8 Plot: sjPlot multiple

plot_models(ols01bs, ols02bs,
            title = "Sales (in mio)",
            show.values = TRUE, 
            show.intercept = FALSE, 
            line.size = 0.1,
            p.shape = TRUE,
            value.size = 3,
            digits = 4) +
  #edit with ggplot2
  scale_y_continuous(limits = c(-0.12,0.12)) +
  theme(
      axis.text.x = element_text(size = 8),
      axis.ticks.x = element_blank(),
      axis.line.x = element_blank(),
      axis.title.x = element_text(size = 8),
      axis.line.y = element_blank(),
      axis.title.y = element_text(size = 8),
      axis.text.y = element_text(size = 8),
      plot.title.position = "plot",
      plot.caption.position = "plot",
      legend.title = element_text(size = 8),
      legend.text = element_text(size = 8),
      legend.key.size = unit(0.5, "cm"),
      legend.spacing = unit(1, "cm"),
      plot.title = element_text(hjust = 0, size = 16, face = "bold"),
      plot.subtitle = element_text(hjust = 0, size = 12),
      plot.caption = element_text(hjust = 0, size = 12),
      # legend.title = element_blank(),
      legend.position = "bottom",
      strip.text.x = element_text(size = 8),
      panel.grid.major.y = element_line(colour = "grey97"),
      panel.ontop = FALSE,
      panel.spacing = unit(1, "lines")
      )

## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.

## Warning: Argument 'df_method' is deprecated. Please use 'ci_method' instead.

## Scale for 'y' is already present. Adding another scale for 'y', which will
## replace the existing scale.

5.9 Plot: `forestmodel`

library(forestmodel)

forest_model(ols01bs, theme = theme_forest())

forest_model(ols02bs, theme = theme_forest())

5.10 Performance: glance

gl01bs <- broom::glance(ols01bs) %>% t() %>% round(digits = 2)
gl02bs <- broom::glance(ols02bs) %>% t() %>% round(digits = 2)

cbind(gl01bs, gl02bs)

##                 [,1]   [,2]
## r.squared       0.18   0.29
## adj.r.squared   0.13   0.25
## sigma           0.41   0.39
## statistic       3.55   6.50
## p.value         0.02   0.00
## df              3.00   3.00
## logLik        -25.42 -21.78
## AIC            60.85  53.57
## BIC            70.51  63.22
## deviance        8.09   7.02
## df.residual    47.00  47.00
## nobs           51.00  51.00

5.11 Performance: check model

pacman::p_load(patchwork, performance)
(
  ch01 <- check_model(ols01bs)
)

(
  ch02 <- check_model(ols02bs)
)

5.12 Performance: compare

#install.packages("performance", repos = "https://easystats.r-universe.dev")
library(performance)
library(see)

(
  comp <- compare_performance(ols01bs, ols02bs)
)

plot(comp)

6 Clearing

#clearing the environment
ls()

##  [1] "ch01"     "ch02"     "comp"     "corr01"   "db01"     "gl01bs"  
##  [7] "gl02bs"   "ols01bs"  "ols01dp"  "ols01es"  "ols02bs"  "ols02dp" 
## [13] "ols02es"  "ols03es"  "ols04es"  "op_sales" "plot01"   "plot02"  
## [19] "plot03"   "summ01"   "summ02"   "summ03"

rm(list=ls())

7 Acknowledgements

7.1 References

Grolemund, G., & Wickham, H. (2017). R for Data Science. O’Reilly Media.
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Wickham et al., (2019). Welcome to the tidyverse. Journal of Open Source Software, 4(43), 1686, https://doi.org/10.21105/joss.01686.
Claus O. Wilke: Data Visualization in R, https://wilkelab.org/SDS375/
Cedric Scherer’s personal blog: https://www.cedricscherer.com/
Kim, A. Y. and Ismay, C. and Kuhn, M. (2021) Take a moderndive into introductory linear regression with R Journal of Open Source Education. 4(41), 115 https://doi.org/10.21105/jose.00115
The R Graph Gallery: https://www.r-graph-gallery.com/index.html
…and other sources i.e. stackoverflow.com, github.com

7.2 Datasets

One Piece by volume: https://listfist.com/list-of-one-piece-volumes
One Piece by chapter: https://listfist.com/list-of-one-piece-manga-chapters
One Piece sales: https://erzat.blog/top-sales-according-to-series-for-the-month-of-september-2021/ and https://twitter.com/WSJ_manga/status/1214168838511702016
VIX index: https://finance.yahoo.com/quote/%5EVIX?p=%5EVIX

Contact me at ap.sulistiono@gmail.com

Basic Modelling

Kelas Data R organised by mof.dac

Arif P. Sulistiono / @arifpras

11 November 2021

1 Prerequisites

1.1 Materials

1.2 Clearing the environment

1.3 Packages and libraries

1.4 Datasets

2 Plotting

2.1 Line chart

2.2 Box plot

2.3 Histogram

3 Descriptive statistics

3.1 Summary: skim

3.2 Summary: stargazer

3.3 Summary: dplyr

3.4 Summary: parameters

4 Correlations

5 Simple Regressions

5.1 Modelling: base

5.2 Modelling: dplyr

5.3 Modelling: estimatr

5.4 Export: stargazer

5.5 Export: texreg

5.6 Export: `lm_robust` & `wordreg`

5.7 Plot: sjPlot single

5.8 Plot: sjPlot multiple

5.9 Plot: `forestmodel`

5.10 Performance: glance

5.11 Performance: check model

5.12 Performance: compare

6 Clearing

7 Acknowledgements

7.1 References

7.2 Datasets

Basic Modelling

Kelas Data R organised by mof.dac

Arif P. Sulistiono / @arifpras

11 November 2021

1 Prerequisites

1.1 Materials

1.2 Clearing the environment

1.3 Packages and libraries

1.4 Datasets

2 Plotting

2.1 Line chart

2.2 Box plot

2.3 Histogram

3 Descriptive statistics

3.1 Summary: skim

3.2 Summary: stargazer

3.3 Summary: dplyr

3.4 Summary: parameters

4 Correlations

5 Simple Regressions

5.1 Modelling: base

5.2 Modelling: dplyr

5.3 Modelling: estimatr

5.4 Export: stargazer

5.5 Export: texreg

5.6 Export: lm_robust & wordreg

5.7 Plot: sjPlot single

5.8 Plot: sjPlot multiple

5.9 Plot: forestmodel

5.10 Performance: glance

5.11 Performance: check model

5.12 Performance: compare

6 Clearing

7 Acknowledgements

7.1 References

7.2 Datasets

5.6 Export: `lm_robust` & `wordreg`

5.9 Plot: `forestmodel`